Instructions to use Humenuik/finetune-gemma3-270m-it-nlp2sql with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Humenuik/finetune-gemma3-270m-it-nlp2sql with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Humenuik/finetune-gemma3-270m-it-nlp2sql")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Humenuik/finetune-gemma3-270m-it-nlp2sql")
model = AutoModelForCausalLM.from_pretrained("Humenuik/finetune-gemma3-270m-it-nlp2sql", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Humenuik/finetune-gemma3-270m-it-nlp2sql with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Humenuik/finetune-gemma3-270m-it-nlp2sql"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Humenuik/finetune-gemma3-270m-it-nlp2sql",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Humenuik/finetune-gemma3-270m-it-nlp2sql

SGLang

How to use Humenuik/finetune-gemma3-270m-it-nlp2sql with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Humenuik/finetune-gemma3-270m-it-nlp2sql" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Humenuik/finetune-gemma3-270m-it-nlp2sql",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Humenuik/finetune-gemma3-270m-it-nlp2sql" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Humenuik/finetune-gemma3-270m-it-nlp2sql",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Humenuik/finetune-gemma3-270m-it-nlp2sql with Docker Model Runner:
```
docker model run hf.co/Humenuik/finetune-gemma3-270m-it-nlp2sql
```

See axolotl config

axolotl version: 0.13.0.dev0

base_model: google/gemma-3-270m-it
model_type: GemmaForCausalLM
tokenizer_type: AutoTokenizer
trust_remote_code: true

datasets:
  - path: Humenuik/train.jsonl
    type: alpaca
    conversation: "alpaca"

dataset_prepared_path:
  /workspace/output/prepared

output_dir: /workspace/output/finetune-gemma3-270m-it

lora_r: 32 # Set the LoRA rank, influencing the capacity of the adapter layers.
lora_alpha: 16 # Adjust the LoRA alpha, a scaling factor for LoRA updates.
lora_dropout: 0.05 # Apply a dropout rate to LoRA layers for regularization.
lora_target_modules:
  - q_proj
  - v_proj
  - k_proj
  - o_proj
  - gate_proj
  - up_proj
  - down_proj # Specify the modules within the model where LoRA adapters will be applied (e.g., attention projections, MLP layers).

gradient_checkpointing: false
micro_batch_size: 8 # Define the size of each micro-batch processed by the GPU.
num_epochs: 2 # Set the number of training epochs.
learning_rate: 0.0002 # Specify the learning rate for the optimizer.
lr_scheduler: cosine # Choose a learning rate scheduler (e.g., cosine).
warmup_steps: 10 # Configure warmup steps for the learning rate scheduler.
batch_size: 24
gradient_checkpointing: true # Enable gradient checkpointing to reduce memory consumption during training.

workspace/output/finetune-gemma3-270m-it

This model is a fine-tuned version of google/gemma-3-270m-it on the Humenuik/train.jsonl dataset.

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0002
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 10
training_steps: 4528

Training results

Framework versions

Transformers 4.55.2
Pytorch 2.7.1+cu126
Datasets 4.0.0
Tokenizers 0.21.4

Downloads last month: 4

Safetensors

Model size

0.3B params

Tensor type

F32

BF16

Model tree for Humenuik/finetune-gemma3-270m-it-nlp2sql

Base model

google/gemma-3-270m

Finetuned

google/gemma-3-270m-it

Finetuned

(1125)

this model